Medical billing company. 2,400 insurance claims monthly. Manual data entry error rate: 13% 312 claims had errors every month. Each required manual correction. THE CLAIMS PROCESSING NIGHTMARE: Every insurance claim required: - Patient information - Diagnosis codes (ICD-10) - Procedure codes (CPT) - Provider information - Insurance policy details Data sources: - Patient intake forms (handwritten) - Doctor's notes (typed or dictated) - Insurance cards (photos) - Previous claims (reference) Billing specialist types everything into claims system. THE ERROR TYPES: 13% error rate broke down as: - Transposition errors: 4% (typing 1234 as 1243) - Wrong code selection: 3% (ICD-10 has 70,000+ codes) - Missing required fields: 2% - Invalid insurance numbers: 2% - Date format errors: 2% THE COST CASCADE: Insurance claim with error: - Rejected automatically - Notification received (3-5 days later) - Specialist investigates error - Correction submitted - Re-adjudication (another 7-10 days) Time to payment: 10-14 days → 20-28 days THE CASH FLOW IMPACT: 312 claims monthly with errors Average claim value: $840 Delayed payment: $262,080 monthly That money was owed. Just delayed 10-18 extra days. At practice's cost of capital: ~$3,200 monthly in delayed cash flow Annual cost: $38,400 just from timing delay THE REWORK COST: Each claim error required: - Error investigation: 15 minutes - Correction and resubmission: 10 minutes - Total: 25 minutes per error 312 errors × 25 minutes = 7,800 minutes monthly = 130 hours 130 hours × $28/hour billing specialist = $3,640 monthly Annual rework cost: $43,680 Total annual cost: $38,400 (delay) + $43,680 (rework) = $82,080 THE SOLUTION I BUILT: Claims data extraction and validation: - Intake forms scanned/photographed - System extracts patient info, insurance details - Validates ICD-10 and CPT codes - Checks insurance number format - Pre-populates claims form - Flags potential errors BEFORE submission Billing specialist reviews pre-filled form, submits.

Hi all, I have tried Llama Cloud and a few other options to try and take a very messy PDF with a mix of typed and hand written words to an excel spreadsheet. These are patient manifests from a hospital with doctor handwriting on them (you can imagine what I am getting) and just can't for the life of me figure out a way to teach these tools to capture the right information. Does anyone have experience with this? I have searched the past posts but I have tried those tools and I feel like how unstructured it is is a mess. Thank you!!

Best way to test our workflows is on ourselves and then wait for the magic to happen. That’s exactly what I’m doing with this setup! Excited to share that I’ve just set up a new automation workflow designed to help with daily content creation! 🎯 🔹 How it works: - Triggers automatically every day at 8:00 AM - Generates content ideas around content creation & automation - Tailored for solopreneurs, entrepreneurs, creators, and digital-first founders - Goal: to make it easier to grow and manage an online presence consistently 🔹 Why this matters:Content consistency is one of the biggest challenges for solopreneurs and digital-first businesses. This workflow eliminates that friction by ensuring fresh, valuable content is ready every day. 💡 This is just the beginning. I’ll be expanding this workflow further with AI-driven personalization, LinkedIn posting, and automation add-ons. 👉 Excited to hear your thoughts — what would you like to see next in this workflow? If you’re excited to see this in action, drop a “creator” in the comments and I’ll share behind-the-scenes updates!

PDFs lock content into complex layouts, making it difficult for LLMs to process text, tables, and images effectively. Dolphin is an open source parsing framework that converts PDFs into structured formats such as Markdown, HTML, LaTeX, and JSON. 🛠️ How It Works 1. Layout analysis - Detects and sequences elements according to the document’s natural reading order. 2. Parallel parsing - Processes each element with specialized prompts tailored to different content types (text blocks, tables, figures, etc.). 🗝️ Key Features - Two-stage “analyze-then-parse” pipeline powered by a single VLM - Strong performance on complex document parsing tasks - Reading-order-aware element sequencing - Specialized prompts for different document elements - Efficient parallel parsing for faster results It’s 100% Open Source 🙌🏻 Github Repo

Yesterday, I had a conversation at my youngest son's hospitality school that left me genuinely shocked. My question: "What's the AI program at your school?" Their answer: "Nothing." My follow-up: "Are you planning to change this for next year?" Their answer: "No." This is happening in the Netherlands. In 2025. In an industry that is being transformed at breakneck speed. The hospitality industry is changing FAST: - AI-powered booking systems are becoming standard - Guest personalization through AI is the new expectation - Revenue management is increasingly automated - Customer service chatbots are handling initial interactions - Operations are being optimized through predictive analytics Yet our hospitality schools are sending students into the workforce with zero AI literacy. No understanding of the tools that will define their careers. No training on how to work alongside these systems. No preparation for what the industry actually looks like today—let alone tomorrow. We're not preparing our young professionals. We're setting them up to play catch-up on day one. To educators: The industry won't wait for curriculum committees. Our students deserve better. To industry leaders: What are you seeing? Are new graduates prepared for the reality of modern hospitality? We need to bridge this gap. Now.